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arXiv:2606. 31694v1 Announce Type: cross Abstract: For robots manipulating open-world objects, tactile representations must generalize to unseen materials.
arXiv:2606. 31451v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) have shown great promise in integrating understanding and generation across diverse modalities.
The paper introduces BIFTA, a Brain‑Inspired Few‑Shot Tactile Adaptation framework that enables a frozen encoder to adapt quickly to an unknown tactile sensor using only a small labeled support set. It preserves pretrained representations via dual‑view statistical memory, builds support‑conditioned spectral graphs to correct sensor‑dependent feature neighborhoods, and employs uncertainty‑gated recurrent propagation to reinforce reliable cross‑query evidence. Benchmarks on three tactile datasets demonstrate that BIFTA dramatically improves adaptation performance, achieving an 87.09% mean Sparsh accuracy on SITR with just 10% labeled data—an increase of 47.22 percentage points over the best prior method.
ControlTac is a two‑stage framework that generates realistic tactile images conditioned on a single reference image, contact force, and contact pose. By incorporating these physical priors, it produces realistic samples across different sensors and captures task‑relevant variations. Experiments in object insertion, imitation learning, and object weighting show that datasets augmented with ControlTac consistently improve performance in dynamic real‑world settings.
arXiv:2606. 11637v1 Announce Type: new Abstract: Touch is a key modality for embodied agents to understand the physical world.
arXiv:2606. 24712v1 Announce Type: cross Abstract: Humans effortlessly locate and identify objects by touch alone, even without vision.